How to import segmentation pre-annotations

In this tutorial, we will show you how to import prediction labels into your Kili project.

You will see these predictions as pre-annotations in your labeling interface, and you will be able to modify or validate them.

We will discover this feature on a segmentation project that is used to work with images or videos with pixel-level annotations.

The data used in this tutorial is from the COCO dataset.

Import an image into your Kili project

Let's first inspect what our annotated image looks like in the COCO dataset.

Raw image	With the annotations

Before starting, we install the requirements:

%pip install matplotlib Pillow kili opencv-python

%matplotlib inline

import functools
import getpass
import json
import os
import time
import urllib.request
from random import randint

import cv2
import matplotlib.pyplot as plt
import numpy as np
import requests
from PIL import Image

from kili.client import Kili

Let's authenticate to Kili:

if "KILI_API_KEY" not in os.environ:
    KILI_API_KEY = getpass.getpass("Please enter your API key: ")
else:
    KILI_API_KEY = os.environ["KILI_API_KEY"]

kili = Kili(api_key=KILI_API_KEY)

Let's create an image project in Kili where we can annotate images with a semantic tool and two classes: HUMAN and MOTORCYCLE at pixel level.

We create the image project with its ontology (json_interface):

json_interface = {
    "jobs": {
        "JOB_0": {
            "mlTask": "OBJECT_DETECTION",
            "tools": ["semantic"],
            "instruction": None,
            "required": 1,
            "isChild": False,
            "content": {
                "categories": {
                    "MOTORCYCLE": {"name": "Motorcycle", "children": [], "color": "#0755FF"},
                    "HUMAN": {"name": "Human", "children": [], "color": "#EEBA00"},
                },
                "input": "radio",
            },
        }
    }
}

project = kili.create_project(
    description="COCO dataset",
    input_type="IMAGE",
    json_interface=json_interface,
    title="Motorcycle annotation",
)

project_id = project["id"]

Then, we upload the image to the project:

external_id = "moto"
content = "https://farm7.staticflickr.com/6153/6181981748_6a225c275d_z.jpg"

kili.append_many_to_dataset(
    project_id=project_id,
    content_array=[content],
    external_id_array=[external_id],
    json_content_array=None,
);

You should now be able to visualize your asset in Kili: